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| #Original code can be found on: https://github.com/black-forest-labs/flux | |
| from dataclasses import dataclass | |
| import torch | |
| from torch import Tensor, nn | |
| from .layers import ( | |
| DoubleStreamBlock, | |
| EmbedND, | |
| LastLayer, | |
| MLPEmbedder, | |
| SingleStreamBlock, | |
| timestep_embedding, | |
| ) | |
| from einops import rearrange, repeat | |
| import comfy.ldm.common_dit | |
| class FluxParams: | |
| in_channels: int | |
| out_channels: int | |
| vec_in_dim: int | |
| context_in_dim: int | |
| hidden_size: int | |
| mlp_ratio: float | |
| num_heads: int | |
| depth: int | |
| depth_single_blocks: int | |
| axes_dim: list | |
| theta: int | |
| patch_size: int | |
| qkv_bias: bool | |
| guidance_embed: bool | |
| class Flux(nn.Module): | |
| """ | |
| Transformer model for flow matching on sequences. | |
| """ | |
| def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): | |
| super().__init__() | |
| self.dtype = dtype | |
| params = FluxParams(**kwargs) | |
| self.params = params | |
| self.patch_size = params.patch_size | |
| self.in_channels = params.in_channels * params.patch_size * params.patch_size | |
| self.out_channels = params.out_channels * params.patch_size * params.patch_size | |
| if params.hidden_size % params.num_heads != 0: | |
| raise ValueError( | |
| f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" | |
| ) | |
| pe_dim = params.hidden_size // params.num_heads | |
| if sum(params.axes_dim) != pe_dim: | |
| raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") | |
| self.hidden_size = params.hidden_size | |
| self.num_heads = params.num_heads | |
| self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) | |
| self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) | |
| self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) | |
| self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) | |
| self.guidance_in = ( | |
| MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() | |
| ) | |
| self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) | |
| self.double_blocks = nn.ModuleList( | |
| [ | |
| DoubleStreamBlock( | |
| self.hidden_size, | |
| self.num_heads, | |
| mlp_ratio=params.mlp_ratio, | |
| qkv_bias=params.qkv_bias, | |
| dtype=dtype, device=device, operations=operations | |
| ) | |
| for _ in range(params.depth) | |
| ] | |
| ) | |
| self.single_blocks = nn.ModuleList( | |
| [ | |
| SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) | |
| for _ in range(params.depth_single_blocks) | |
| ] | |
| ) | |
| if final_layer: | |
| self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) | |
| def forward_orig( | |
| self, | |
| img: Tensor, | |
| img_ids: Tensor, | |
| txt: Tensor, | |
| txt_ids: Tensor, | |
| timesteps: Tensor, | |
| y: Tensor, | |
| guidance: Tensor = None, | |
| control=None, | |
| transformer_options={}, | |
| ) -> Tensor: | |
| patches_replace = transformer_options.get("patches_replace", {}) | |
| if img.ndim != 3 or txt.ndim != 3: | |
| raise ValueError("Input img and txt tensors must have 3 dimensions.") | |
| # running on sequences img | |
| img = self.img_in(img) | |
| vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) | |
| if self.params.guidance_embed: | |
| if guidance is None: | |
| raise ValueError("Didn't get guidance strength for guidance distilled model.") | |
| vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) | |
| vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) | |
| txt = self.txt_in(txt) | |
| ids = torch.cat((txt_ids, img_ids), dim=1) | |
| pe = self.pe_embedder(ids) | |
| blocks_replace = patches_replace.get("dit", {}) | |
| for i, block in enumerate(self.double_blocks): | |
| if ("double_block", i) in blocks_replace: | |
| def block_wrap(args): | |
| out = {} | |
| out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"]) | |
| return out | |
| out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe}, {"original_block": block_wrap}) | |
| txt = out["txt"] | |
| img = out["img"] | |
| else: | |
| img, txt = block(img=img, txt=txt, vec=vec, pe=pe) | |
| if control is not None: # Controlnet | |
| control_i = control.get("input") | |
| if i < len(control_i): | |
| add = control_i[i] | |
| if add is not None: | |
| img += add | |
| img = torch.cat((txt, img), 1) | |
| for i, block in enumerate(self.single_blocks): | |
| if ("single_block", i) in blocks_replace: | |
| def block_wrap(args): | |
| out = {} | |
| out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"]) | |
| return out | |
| out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe}, {"original_block": block_wrap}) | |
| img = out["img"] | |
| else: | |
| img = block(img, vec=vec, pe=pe) | |
| if control is not None: # Controlnet | |
| control_o = control.get("output") | |
| if i < len(control_o): | |
| add = control_o[i] | |
| if add is not None: | |
| img[:, txt.shape[1] :, ...] += add | |
| img = img[:, txt.shape[1] :, ...] | |
| img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) | |
| return img | |
| def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs): | |
| bs, c, h, w = x.shape | |
| patch_size = self.patch_size | |
| x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) | |
| img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) | |
| h_len = ((h + (patch_size // 2)) // patch_size) | |
| w_len = ((w + (patch_size // 2)) // patch_size) | |
| img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) | |
| img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) | |
| img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) | |
| img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) | |
| txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) | |
| out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options) | |
| return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] | |